Oxygen&Voltage_gradients

Introduction

This rmd. file imports oxygen and voltage data from Google sheet Data is used to plot a oxygen and voltage gradient map of the winogradsky column Exponential and linear models were used to analyze data

#Loading Packages

library(tidyverse) 
library(googledrive) 
library(googlesheets4)
#library(ggplot2)
library(knitr)
library(minpack.lm)
library(broom)
library(purrr)
library(lme4)
library(patchwork)

#Importing from google sheet

googlesheets4::gs4_deauth()

CleanData_Oxygen <- googlesheets4::read_sheet("https://docs.google.com/spreadsheets/d/1hNakCyqPsTNRFpf1lGjoBQ-papyZ6xHKT6mcvh3LMb8/edit?pli=1&gid=842906991#gid=842906991", "CleanData_Oxygen") |>
  mutate(Sample = as.character(Sample))

✔ Reading from Microcosms.
✔ Range ''CleanData_Oxygen''.

kable(CleanData_Oxygen) 

Sample	Depth_cm	DO_µg_L	temp_C
1004	0.0	276.0	21.2
1004	6.2	276.0	21.2
1004	8.4	275.0	21.2
1004	13.1	6.3	21.2
1004	22.0	3.4	21.2
1003	0.0	101.0	20.0
1003	5.8	62.0	20.0
1003	8.4	52.0	20.0
1003	13.1	29.0	20.0
1003	21.3	21.0	20.0
1002	0.0	80.0	19.8
1002	6.6	45.0	19.8
1002	9.3	26.0	19.8
1002	15.1	21.0	19.8
1002	21.9	19.0	19.8
1001_C	0.0	190.0	19.0
1001_C	6.9	106.0	19.0
1001_C	10.8	99.0	19.0
1001_C	16.6	57.0	19.0
1001_C	22.7	25.0	19.0
1001_W	0.0	103.0	19.0
1001_W	6.7	34.0	19.0
1001_W	10.2	21.0	19.0
1001_W	15.2	11.0	19.0
1001_W	22.8	8.0	19.0

googlesheets4::gs4_deauth()

CleanData_Voltage <- googlesheets4::read_sheet("https://docs.google.com/spreadsheets/d/1hNakCyqPsTNRFpf1lGjoBQ-papyZ6xHKT6mcvh3LMb8/edit?pli=1&gid=842906991#gid=842906991", "CleanData_Voltage") |>
  mutate(Sample = as.character(Sample))

✔ Reading from Microcosms.
✔ Range ''CleanData_Voltage''.

kable(CleanData_Voltage)

Sample	Depth_cm	Voltage_Water_10s_mv	Voltage_Water_Max_mv	Voltage_Tap_10s_mv	Voltage_Tap_Max_mv
1004	0.0	NA	NA	121.0	121.0
1004	6.2	160.2	190	-168.0	-168.0
1004	8.4	74.4	79	84.0	122.0
1004	13.1	-55.0	58	43.0	51.0
1004	22.0	-200.0	204	84.0	102.0
1003	0.0	NA	NA	74.0	300.0
1003	5.8	27.0	27	-13.0	26.0
1003	8.4	59.0	83	57.0	57.0
1003	13.1	-20.0	101	-94.0	-136.0
1003	21.3	-25.0	-30	161.0	177.0
1002	0.0	NA	NA	51.0	199.0
1002	6.6	-39.0	-40	13.0	30.0
1002	9.3	-4.3	30	196.0	199.0
1002	15.1	24.0	-4	16.0	25.0
1002	21.9	27.0	28	194.0	199.0
1001_C	0.0	NA	NA	187.0	187.0
1001_C	6.9	-10.0	-22	76.0	300.0
1001_C	10.8	25.0	60	-2.8	-2.8
1001_C	16.6	-6.0	-23	-10.5	-3.0
1001_C	22.7	6.0	48	168.0	174.0
1001_W	0.0	NA	NA	187.0	187.0
1001_W	6.7	16.0	-9	13.0	170.0
1001_W	10.2	31.0	33	-48.0	-48.0
1001_W	15.2	34.0	40	24.0	24.0
1001_W	22.8	40.0	-17	72.0	72.0

Combine O2 & Voltage data by sample & depth

Gradients <- left_join(x = CleanData_Oxygen, y = CleanData_Voltage, by = c("Sample", "Depth_cm"))

#Oxygen Gradient Plot

ggplot(data = CleanData_Oxygen) +
geom_point(aes(y = DO_µg_L, x = Depth_cm)) + 
  #scale_y_reverse() +
  #labs( title= "Oxygen Gradients", caption= "Figure X. Dissolved Oxygen (DO) (µg/L) measured in four Winogradsky colums at 5 depths (cm)") 
  #+ theme(plot.caption= element_text(size = 11, hjust=0)) +
  #geom_path(aes(y = Depth_cm, x = DO_µg_L)) +
  facet_grid(cols = vars(Sample)) +
  theme_bw()

#+ scale_y_continuous(limits = c(0, 300))

#define exponential decay function for data fitting.
exp_decay <- function(x, i, mu){y = i * exp(mu * x)}


O2_nest <- CleanData_Oxygen |>  #alternate forward pipe is %>% loaded with tidyverse
  nest(.by = "Sample") |>
  mutate(DecayFit = purrr::map(data, ~nlsLM(DO_µg_L ~ exp_decay(x = Depth_cm, i, mu),
                                            data = .x)),
         DecayTidy = purrr::map(DecayFit, tidy),
         DecayParam = purrr::map(DecayFit, glance),
         DecayPredict = purrr::map(DecayFit, augment)
         )

Warning: There were 5 warnings in `mutate()`.
The first warning was:
ℹ In argument: `DecayFit = purrr::map(...)`.
Caused by warning in `nlsLM()`:
! No starting values specified for some parameters.
Initializing ‘i’, ‘mu’ to '1.'.
Consider specifying 'start' or using a selfStart model
ℹ Run ]8;;ide:run:dplyr::last_dplyr_warnings()dplyr::last_dplyr_warnings()]8;; to see the 4 remaining warnings.

O2_nest |>
  unnest(cols = c(DecayPredict)) |>
  ggplot() +
  geom_point(aes(x = Depth_cm, y = DO_µg_L)) +
  geom_line(aes(x = Depth_cm, y = .fitted)) +
  geom_point(aes(x = Depth_cm, y = .resid), colour = "red") +
  facet_grid(cols = vars(unlist(Sample))) +
  theme_bw()

O2_nest |>
unnest(cols = c(DecayTidy)) |>
 select(-c(data, DecayFit, DecayParam, DecayPredict)) |>
  select(-c(statistic)) |>
  pivot_wider(id_cols = Sample, names_from = term, values_from = c(estimate, std.error, p.value)) |>
  kable()

Sample	estimate_i	estimate_mu	std.error_i	std.error_mu	p.value_i	p.value_mu
1004	326.62312	-0.0779518	101.553082	0.0494649	0.0487222	0.2131346
1003	100.85694	-0.0833277	3.557277	0.0059295	0.0000963	0.0007804
1002	78.61257	-0.0897172	6.441339	0.0139884	0.0011845	0.0076805
1001_C	190.11905	-0.0743515	10.391455	0.0078457	0.0003563	0.0024909
1001_W	102.49196	-0.1553412	3.251191	0.0097456	0.0000701	0.0005369

Voltage Gradient Plot

ggplot(data = Gradients) +
  geom_point(aes(x = Depth_cm, y = Voltage_Water_10s_mv)) + 
  facet_grid(cols = vars(Sample)) + 
  theme_bw()

  Linear_fit <- Gradients %>% 
  group_by(Sample) %>%
  group_modify(~ {

  linear <- lm(Voltage_Water_10s_mv ~ Depth_cm, data = .x, na.action = na.exclude)
  augment(linear, data = .x)}) %>%
  ungroup()


  Linear_fit |>   
    ggplot(aes(x = Depth_cm, y = Voltage_Water_10s_mv)) +
    geom_point() +
    geom_line(aes(y = .fitted)) +
    #Showing residuals
    #geom_point(aes(x = Depth_cm, y = .resid), colour = "red") +
    geom_segment(aes(xend = Depth_cm, yend = .fitted), color = "red") +
    facet_grid(cols = vars(unlist(Sample))) +
    theme_bw()

NA
NA

 
 Linear_fit_results <- Linear_fit |>
   group_by(Sample) |>
   group_modify(~ {
    linear_model <- lm(Voltage_Water_10s_mv ~ Depth_cm, data = .x)
    tidy_model <- tidy(linear_model)  
    tidy_model$Sample <- unique(.x$Sample)  
    return(tidy_model)  
   }) |>
  ungroup() |>
  # Filter for 'Depth_cm' coefficient (we are examining Voltage in terms of Depth_cm)
  filter(term == "Depth_cm")  

Warning: Unknown or uninitialised column: `Sample`.Warning: Unknown or uninitialised column: `Sample`.Warning: Unknown or uninitialised column: `Sample`.Warning: Unknown or uninitialised column: `Sample`.Warning: Unknown or uninitialised column: `Sample`.

# Selecting to include Sample, estimate of slope, std.error, and p.value in table output
Linear_fit_results |>
  select(Sample, estimate, std.error, p.value) 

 
#Displaying the table and specifying sig digits shown
 Linear_fit |>
  kable(digits=3)

Sample	Depth_cm	DO_µg_L	temp_C	Voltage_Water_10s_mv	Voltage_Water_Max_mv	Voltage_Tap_10s_mv	Voltage_Tap_Max_mv	.fitted	.resid	.hat	.sigma	.cooksd	.std.resid
1001_C	0.0	190.0	19.0	NA	NA	187.0	187.0	NA	NA	0.000	19.193	NA	NA
1001_C	6.9	106.0	19.0	-10.0	-22	76.0	300.0	2.523	-12.523	0.628	17.748	0.967	-1.070
1001_C	10.8	99.0	19.0	25.0	60	-2.8	-2.8	3.174	21.826	0.333	4.714	0.485	1.393
1001_C	16.6	57.0	19.0	-6.0	-23	-10.5	-3.0	4.142	-10.142	0.289	24.334	0.080	-0.627
1001_C	22.7	25.0	19.0	6.0	48	168.0	174.0	5.161	0.839	0.750	27.092	0.011	0.087
1001_W	0.0	103.0	19.0	NA	NA	187.0	187.0	NA	NA	0.000	5.575	NA	NA
1001_W	6.7	34.0	19.0	16.0	-9	13.0	170.0	21.056	-5.056	0.587	0.463	1.418	-1.412
1001_W	10.2	21.0	19.0	31.0	33	-48.0	-48.0	25.637	5.363	0.335	4.349	0.350	1.180
1001_W	15.2	11.0	19.0	34.0	40	24.0	24.0	32.180	1.820	0.265	7.593	0.026	0.381
1001_W	22.8	8.0	19.0	40.0	-17	72.0	72.0	42.127	-2.127	0.813	6.164	1.689	-0.882
1002	0.0	80.0	19.8	NA	NA	51.0	199.0	NA	NA	0.000	17.362	NA	NA
1002	6.6	45.0	19.8	-39.0	-40	13.0	30.0	-24.681	-14.319	0.568	11.331	1.034	-1.255
1002	9.3	26.0	19.8	-4.3	30	196.0	199.0	-13.838	9.538	0.362	21.457	0.134	0.688
1002	15.1	21.0	19.8	24.0	-4	16.0	25.0	9.455	14.545	0.275	17.632	0.184	0.984
1002	21.9	19.0	19.8	27.0	28	194.0	199.0	36.764	-9.764	0.795	11.736	2.993	-1.242
1003	0.0	101.0	20.0	NA	NA	74.0	300.0	NA	NA	0.000	30.529	NA	NA
1003	5.8	62.0	20.0	27.0	27	-13.0	26.0	39.506	-12.506	0.540	39.039	0.214	-0.604
1003	8.4	52.0	20.0	59.0	83	57.0	57.0	27.527	31.473	0.351	18.369	0.443	1.280
1003	13.1	29.0	20.0	-20.0	101	-94.0	-136.0	5.873	-25.873	0.256	31.044	0.167	-0.983
1003	21.3	21.0	20.0	-25.0	-30	161.0	177.0	-31.906	6.906	0.852	39.258	0.999	0.589
1004	0.0	276.0	21.2	NA	NA	121.0	121.0	NA	NA	0.000	34.359	NA	NA
1004	6.2	276.0	21.2	160.2	190	-168.0	-168.0	132.396	27.804	0.513	27.789	0.710	1.160
1004	8.4	275.0	21.2	74.4	79	84.0	122.0	83.803	-9.403	0.360	47.147	0.033	-0.342
1004	13.1	6.3	21.2	-55.0	58	43.0	51.0	-20.009	-34.991	0.253	26.867	0.235	-1.178
1004	22.0	3.4	21.2	-200.0	204	84.0	102.0	-216.590	16.590	0.873	13.735	6.342	1.357

NA

Oxygen vs Voltage Graph

    Oxygen <- ggplot(data = Gradients) +
      geom_point(aes(x = Depth_cm, y = DO_µg_L)) +
      facet_grid(cols = vars(Sample)) + 
      theme_bw()

    Voltage <- ggplot(data = Gradients) +
      geom_point(aes(x = Depth_cm, y = Voltage_Water_10s_mv)) +
      facet_grid(cols = vars(Sample)) + 
      theme_bw()
      
  print(Oxygen/Voltage)

NA
NA

Can only import one image at a time right now, we could choose some notable ones to display?